Computer Science and Electrical Engineering
University of Maryland, Baltimore County
Prediction Markets for Fun, Feedback and the Future
Dr. Sanmay Das
Rensselaer Polytechnic Institute
10:00-11:00am Thursday 17 March 2011
room 456 ITE, UMBC
Prediction markets, when they work well, solve a fundamental problem: how to aggregate individual beliefs into a meaningful quantitative estimate of the probability that a given event will occur. They also provide incentives for people to disseminate privately-held information. I will describe one way to help these markets work better: incorporating a learning agent who provides liquidity, called a market maker. Along the way, the design of this agent raises and solves some fundamental problems in reinforcement learning and Bayesian reasoning. I will also discuss the deployment of this market-making agent in two different settings with human participants. One of these settings is a novel experiment for comparing market structures. Another one, the RPI Instructor Rating Market, allows students to trade on the ratings their professors will receive, thus providing dynamic feedback to instructors on the progress of their classes; we find that market prices are, in fact, better than past ratings at predicting future ratings.
Joint work with Aseem Brahma, Mithun Chakraborty, Allen Lavoie, Malik Magdon-Ismail, and Yonatan Naamad.
Sanmay Das is an Assistant Professor of Computer Science at Rensselaer Polytechnic Institute. He received his Ph.D (2006) and S.M. (2003) degrees from MIT, where he was a student of Tomaso Poggio and Andrew Lo. Prior to that, he received his A.B. in Computer Science from Harvard College (2001). His research focuses on learning in social and economic systems. He has received an NSF CAREER award, is co-author on a paper nominated for the AAMAS Best Student Paper award, and has served as program co-chair for AMMA and workshops chair for the ACM EC conference.